Local Ranking Explanation for Genetic Programming Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems
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gp-bibliography.bib Revision:1.8081
- @InProceedings{wang:2022:GECCO2,
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author = "Shaolin Wang and Yi Mei and Mengjie Zhang",
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title = "Local Ranking Explanation for Genetic Programming
Evolved Routing Policies for Uncertain Capacitated Arc
Routing Problems",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference",
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year = "2022",
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editor = "Alma Rahat and Jonathan Fieldsend and
Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and
Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and
Erik Hemberg and Christopher Cleghorn and Chao-li Sun and
Georgios Yannakakis and Nicolas Bredeche and
Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and
Sebastian Risi and Laetitia Jourdan and
Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and
John Woodward and Malcolm Heywood and Elizabeth Wanner and
Leonardo Trujillo and Domagoj Jakobovic and
Risto Miikkulainen and Bing Xue and Aneta Neumann and
Richard Allmendinger and Inmaculada Medina-Bulo and
Slim Bechikh and Andrew M. Sutton and
Pietro Simone Oliveto",
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pages = "314--322",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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note = "Best Paper Award ECOM Track",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Combinatorial Optimization and Metaheuristics,
uncertain capacitated Arc routing problem, local
explanation, hyper-heuristic",
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isbn13 = "978-1-4503-9237-2",
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DOI = "doi:10.1145/3512290.3528723",
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abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP)
is a well-known combinatorial optimisation problem that
has many real-world applications. Genetic Programming
is usually used to handle UCARP by evolving effective
routing policies, which can respond to the uncertain
environment in real-time. Previous studies mainly focus
on the effectiveness of the routing policies but ignore
the interpretability. we focus on post-hoc
interpretability, which explains a pre-trained complex
routing policy. Unlike the existing explanation methods
for classification/regression models, the behaviour of
a routing policy is characterised as a ranking process
rather than predicting a single output. To address this
issue, this paper proposes a Local Ranking Explanation
(LRE) method for GP-evolved routing policies for UCARP.
Given a UCARP decision situation, LRE trains a linear
model that gives the same ranks of the candidate tasks
as those of the explained routing policy. The
experimental results demonstrate that LRE can obtain
more interpretable linear models that have highly
correlated and consistent behaviours with the original
routing policy in most decision situations. By
analysing coefficients and attribute importance of the
linear model, we managed to provide a local explanation
of the original routing policy in a decision
situation.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Shaolin Wang
Yi Mei
Mengjie Zhang
Citations